The Experts below are selected from a list of 14931 Experts worldwide ranked by ideXlab platform
Dror Fixler - One of the best experts on this subject based on the ideXlab platform.
-
the Squared Distance approach to frequency domain time resolved fluorescence analysis
Journal of Biophotonics, 2019Co-Authors: Gilad Yahav, Hilel Hagai Diamandi, Eyal Preter, Dror FixlerAbstract:A frequency-domain (FD) analysis of fluorescence lifetime (FLT) is a unique and rapid method for cellular and intracellular classifications that can serve for medical diagnostics purposes. Nevertheless, its data analysis process demands nonlinear fitting algorithms that may distort the resolution of the FLT data and hence diminish the classification ability of the method. This research suggests a sample classification technique that is unaffected by the analysis process as it is based on the Squared Distance (D2 ) between the raw frequency response data (FRD). In addition, it presents the theory behind this technique and its validation in two simulated data sets of six groups with similar widely and closely spaced FLT data as well as in experimental data of 43 samples from bacterial and viral infected and non-infected patients. In the two simulated tests, the classification accuracy was above 95% for all six groups. In the experimental data, the classification of 41 out of 43 samples matched earlier report and 29 out of 31 agreed with preliminary physician diagnosis. The D2 approach has the potential to promote FD-time resolved fluorescence measurements as a medical diagnostic technique with high specifity and high sensitivity for many of today's conventional diagnostic procedures.
-
The Squared Distance approach to frequency domain time‐resolved fluorescence analysis
Journal of biophotonics, 2019Co-Authors: Gilad Yahav, Hilel Hagai Diamandi, Eyal Preter, Dror FixlerAbstract:A frequency-domain (FD) analysis of fluorescence lifetime (FLT) is a unique and rapid method for cellular and intracellular classifications that can serve for medical diagnostics purposes. Nevertheless, its data analysis process demands nonlinear fitting algorithms that may distort the resolution of the FLT data and hence diminish the classification ability of the method. This research suggests a sample classification technique that is unaffected by the analysis process as it is based on the Squared Distance (D2 ) between the raw frequency response data (FRD). In addition, it presents the theory behind this technique and its validation in two simulated data sets of six groups with similar widely and closely spaced FLT data as well as in experimental data of 43 samples from bacterial and viral infected and non-infected patients. In the two simulated tests, the classification accuracy was above 95% for all six groups. In the experimental data, the classification of 41 out of 43 samples matched earlier report and 29 out of 31 agreed with preliminary physician diagnosis. The D2 approach has the potential to promote FD-time resolved fluorescence measurements as a medical diagnostic technique with high specifity and high sensitivity for many of today's conventional diagnostic procedures.
-
Frequency domain analysis of time-resolved fluorescence measurements using the Squared Distance approach
Nanoscale Imaging Sensing and Actuation for Biomedical Applications XVI, 2019Co-Authors: Gilad Yahav, Hilel Hagai Diamandi, Eyal Preter, Dror FixlerAbstract:Frequency domain analysis of time-resolved fluorescence measurements (TRFM) is an extremely rapid technique for medical diagnostics thanks to its unique sensitivity to a wide variety of physical and chemical features. Nevertheless, the determination of the underlying fluorescence lifetime (FLT) data of samples by their frequency response data (FRD), demands fitting algorithms. Therefore, the interpretation of the precise changes in the FLT of complex environments in term of biochemical processes is a challenge as it involves uncertainties associated with the chosen fitting algorithm. This research suggests a novel characterization procedure based on the Squared Distance (D2) between the FRD of the samples that avoid the inherent blurring caused by the transformation of the FRD into FLT data. The D2 approach was validated through simulated data of 6 classes with similar FLT characteristics, where the accuracy of D2 classification was about 96%. In addition, this approach was tested on experimental FRD from 43 individual samples that their preliminary physician diagnosis divided them into 4 groups: 5 healthy samples served as controls, 9 samples diagnosed with diverse types of bacteria, 16 samples diagnosed with diverse types of viruses and 13 samples were negatives to any bacterial or viral infection, although presenting related symptoms. Using the D2 analysis, the classification of 28/30 matched the physician diagnosis and the classification of 41/43 samples matched earlier report. In conclusion, this work demonstrated that the D2 model can aid in disease identification and increase the specificity and sensitivity of conventional medical procedures or TRFM-based diagnosis.
Helmut Pottmann - One of the best experts on this subject based on the ideXlab platform.
-
Fitting B-Spline Curves to Point Clouds by Squared Distance Minimization
2006Co-Authors: Wenping Wang, Helmut PottmannAbstract:Computing a curve to approximate data points is a problem encountered frequently in many applications in computer graphics, computer vision, CAD/CAM, and image processing. We present a novel and efficient method, calledSquared Distance minimization (SDM), for computing a planar B-spline curve, closed or open, to approximate a target shape defined by a point cloud, i.e., a set of unorganized, possibly noisy data points. We show that SDM outperforms significantly other optimization methods used currently in common practice of curve fitting. In SDM a B-spline curve starts from some properly specified initial shape and converges towards the target shape through iterative quadratic minimization of the fitting error. Our contribution is the introduction of a new fitting error term, called the Squared Distance (SD) error term, defined by a quadratic approximant of Squared Distances from data points to a fitting curve. The SD error term measures faithfully the geometric Distance between a fitting curve and a target shape, thus leading to faster and more stable convergence than the point Distance (PD) error term, which is commonly used in computer graphics and CAGD, and the tangent Distance (TD) error term, which is adopted in the computer vision community. To provide a theoretical explanation of the superior performance of SDM, we formulate the B-spline curve fitting problem as a nonlinear least squares problem and conclude that SDM is a quasi-Newton method, which employs a carefully chosen positive definite approximant to the true Hessian of the objective function. Furthermore, we show that the method based on the TD error term is a Gauss-Newton iteration which is unstable for target shapes with corners, whereas optimization based on the PD error term is the alternating method that is known to have linear convergence.
-
Fitting B-spline curves to point clouds by curvature-based Squared Distance minimization
ACM Transactions on Graphics, 2006Co-Authors: Wenping Wang, Helmut Pottmann, Yang LiuAbstract:Computing a curve to approximate data points is a problem encountered frequently in many applications in computer graphics, computer vision, CAD/CAM, and image processing. We present a novel and efficient method, called Squared Distance minimization (SDM), for computing a planar B-spline curve, closed or open, to approximate a target shape defined by a point cloud, that is, a set of unorganized, possibly noisy data points. We show that SDM significantly outperforms other optimization methods used currently in common practice of curve fitting. In SDM, a B-spline curve starts from some properly specified initial shape and converges towards the target shape through iterative quadratic minimization of the fitting error. Our contribution is the introduction of a new fitting error term, called the Squared Distance (SD) error term, defined by a curvature-based quadratic approximant of Squared Distances from data points to a fitting curve. The SD error term faithfully measures the geometric Distance between a fitting curve and a target shape, thus leading to faster and more stable convergence than the point Distance (PD) error term, which is commonly used in computer graphics and CAGD, and the tangent Distance (TD) error term, which is often adopted in the computer vision community. To provide a theoretical explanation of the superior performance of SDM, we formulate the B-spline curve fitting problem as a nonlinear least squares problem and conclude that SDM is a quasi-Newton method which employs a curvature-based positive definite approximant to the true Hessian of the objective function. Furthermore, we show that the method based on the TD error term is a Gauss-Newton iteration, which is unstable for target shapes with high curvature variations, whereas optimization based on the PD error term is the alternating method that is known to have linear convergence.
-
registration of point cloud data from a geometric optimization perspective
ACM International Conference Proceeding Series 71 pp. 22-31. (2004), 2004Co-Authors: Niloy J Mitra, Helmut Pottmann, Natasha Gelfand, Leonidas J GuibasAbstract:We propose a framework for pairwise registration of shapes represented by point cloud data (PCD). We assume that the points are sampled from a surface and formulate the problem of aligning two PCDs as a minimization of the Squared Distance between the underlying surfaces. Local quadratic approximants of the Squared Distance function are used to develop a linear system whose solution gives the best aligning rigid transform for the given pair of point clouds. The rigid transform is applied and the linear system corresponding to the new orientation is build. This process is iterated until it converges. The point-to-point and the point-to-plane Iterated Closest Point (ICP) algorithms can be treated as special cases in this framework. Our algorithm can align PCDs even when they are placed far apart, and is experimentally found to be more stable than point-to-plane ICP. We analyze the convergence behavior of our algorithm and of point-to-point and point-to-plane ICP under our proposed framework, and derive bounds on their rate of convergence. We compare the stability and convergence properties of our algorithm with other registration algorithms on a variety of scanned data. © The Eurographics Association 2004.
-
registration of point cloud data from a geometric optimization perspective
Symposium on Geometry Processing, 2004Co-Authors: Niloy J Mitra, Helmut Pottmann, Natasha Gelfand, Leonidas J GuibasAbstract:We propose a framework for pairwise registration of shapes represented by point cloud data (PCD). We assume that the points are sampled from a surface and formulate the problem of aligning two PCDs as a minimization of the Squared Distance between the underlying surfaces. Local quadratic approximants of the Squared Distance function are used to develop a linear system whose solution gives the best aligning rigid transform for the given pair of point clouds. The rigid transform is applied and the linear system corresponding to the new orientation is build. This process is iterated until it converges. The point-to-point and the point-to-plane Iterated Closest Point (ICP) algorithms can be treated as special cases in this framework. Our algorithm can align PCDs even when they are placed far apart, and is experimentally found to be more stable than point-to-plane ICP. We analyze the convergence behavior of our algorithm and of point-to-point and point-to-plane ICP under our proposed framework, and derive bounds on their rate of convergence. We compare the stability and convergence properties of our algorithm with other registration algorithms on a variety of scanned data.
-
registration without icp
Computer Vision and Image Understanding, 2004Co-Authors: Helmut Pottmann, Stefan Leopoldseder, Michael HoferAbstract:We present a new approach to the geometric alignment of a point cloud to a surface and to related registration problems. The standard algorithm is the familiar ICP algorithm. Here, we provide an alternative concept which relies on instantaneous kinematics and on the geometry of the Squared Distance function of a surface. The proposed algorithm exhibits faster convergence than ICP: this is supported both by results of a local convergence analysis and by experiments.
David Thambiratnam - One of the best experts on this subject based on the ideXlab platform.
-
Field validation of controlled Monte Carlo data generation for statistical damage identification employing Mahalanobis Squared Distance
Structural Health Monitoring: An International Journal, 2014Co-Authors: Theanh Nguyen, Tommy H.t. Chan, David ThambiratnamAbstract:This article presents the field applications and validations for the controlled Monte Carlo data generation scheme. This scheme was previously derived to assist the Mahalanobis Squared Distance–based damage identification method to cope with data-shortage problems which often cause inadequate data multinormality and unreliable identification outcome. To do so, real-vibration datasets from two actual civil engineering structures with such data (and identification) problems are selected as the test objects which are then shown to be in need of enhancement to consolidate their conditions. By utilizing the robust probability measures of the data condition indices in controlled Monte Carlo data generation and statistical sensitivity analysis of the Mahalanobis Squared Distance computational system, well-conditioned synthetic data generated by an optimal controlled Monte Carlo data generation configurations can be unbiasedly evaluated against those generated by other set-ups and against the original data. The analysis results reconfirm that controlled Monte Carlo data generation is able to overcome the shortage of observations, improve the data multinormality and enhance the reliability of the Mahalanobis Squared Distance–based damage identification method particularly with respect to false-positive errors. The results also highlight the dynamic structure of controlled Monte Carlo data generation that makes this scheme well adaptive to any type of input data with any (original) distributional condition.
-
Controlled Monte Carlo data generation for statistical damage identification employing Mahalanobis Squared Distance
Structural Health Monitoring: An International Journal, 2014Co-Authors: Theanh Nguyen, Tommy H.t. Chan, David ThambiratnamAbstract:The use of Mahalanobis Squared Distance–based novelty detection in statistical damage identification has become increasingly popular in recent years. The merit of the Mahalanobis Squared Distance–based method is that it is simple and requires low computational effort to enable the use of a higher dimensional damage-sensitive feature, which is generally more sensitive to structural changes. Mahalanobis Squared Distance–based damage identification is also believed to be one of the most suitable methods for modern sensing systems such as wireless sensors. Although possessing such advantages, this method is rather strict with the input requirement as it assumes the training data to be multivariate normal, which is not always available particularly at an early monitoring stage. As a consequence, it may result in an ill-conditioned training model with erroneous novelty detection and damage identification outcomes. To date, there appears to be no study on how to systematically cope with such practical issues esp...
-
Controlled Monte Carlo data generation for statistical damage identification employing Mahalanobis Squared Distance
Science & Engineering Faculty, 2014Co-Authors: Theanh Nguyen, Tommy H.t. Chan, David ThambiratnamAbstract:The use of Mahalanobis Squared Distance–based novelty detection in statistical damage identification has become increasingly popular in recent years. The merit of the Mahalanobis Squared Distance–based method is that it is simple and requires low computational effort to enable the use of a higher dimensional damage-sensitive feature, which is generally more sensitive to structural changes. Mahalanobis Squared Distance–based damage identification is also believed to be one of the most suitable methods for modern sensing systems such as wireless sensors. Although possessing such advantages, this method is rather strict with the input requirement as it assumes the training data to be multivariate normal, which is not always available particularly at an early monitoring stage. As a consequence, it may result in an ill-conditioned training model with erroneous novelty detection and damage identification outcomes. To date, there appears to be no study on how to systematically cope with such practical issues especially in the context of a statistical damage identification problem. To address this need, this article proposes a controlled data generation scheme, which is based upon the Monte Carlo simulation methodology with the addition of several controlling and evaluation tools to assess the condition of output data. By evaluating the convergence of the data condition indices, the proposed scheme is able to determine the optimal setups for the data generation process and subsequently avoid unnecessarily excessive data. The efficacy of this scheme is demonstrated via applications to a benchmark structure data in the field.
Bruno Lévy - One of the best experts on this subject based on the ideXlab platform.
-
Fitting polynomial surfaces to triangular meshes with Voronoi Squared Distance minimization
Engineering with Computers, 2014Co-Authors: Vincent Nivoliers, Bruno LévyAbstract:This paper introduces Voronoi Squared Distance minimization (VSDM), an algorithm that fits a surface to an input mesh. VSDM minimizes an objective function that corresponds to a Voronoi-based approximation of the overall Squared Distance function between the surface and the input mesh (SDM). This objective function is a generalization of the one minimized by centroidal Voronoi tessellation, and can be minimized by a quasi-Newton solver. VSDM naturally adapts the orientation of the mesh elements to best approximate the input, without estimating any differential quantities. Therefore, it can be applied to triangle soups or surfaces with degenerate triangles, topological noise and sharp features. Applications of fitting quad meshes and polynomial surfaces to input triangular meshes are demonstrated.
-
Fitting Polynomial Volumes to Surface Meshes with Voronoï Squared Distance Minimization
Computer Graphics Forum, 2013Co-Authors: Gilles-philippe Paillé, Pierre Poulin, Bruno LévyAbstract:Also Proceedings of the Annual Symposium on Geometry ProcessingInternational audienceWe propose a method for mapping polynomial volumes. Given a closed surface and an initial template volume grid, our method deforms the template grid by fitting its boundary to the input surface while minimizing a vol- ume distortion criterion. The result is a point-to-point map distorting linear cells into curved ones. Our method is based on several extensions of Voronoi Squared Distance Minimization (VSDM) combined with a higher-order finite element formulation of the deformation energy. This allows us to globally optimize the mapping without prior parameterization. The anisotropic VSDM formulation allows for sharp and semi-sharp features to be im- plicitly preserved without tagging. We use a hierarchical finite element function basis that selectively adapts to the geometric details. This makes both the method more efficient and the representation more compact. We ap- ply our method to geometric modeling applications in computer-aided design and computer graphics, including mixed-element meshing, mesh optimization, subdivision volume fitting, and shell meshing
-
Fitting Polynomial Volumes to Surface Meshes with Voronoï Squared Distance Minimization
Computer Graphics Forum, 2013Co-Authors: Gilles-philippe Paillé, Pierre Poulain, Bruno LévyAbstract:We propose a method for mapping polynomial volumes. Given a closed surface and an initial template volume grid, our method deforms the template grid by fitting its boundary to the input surface while minimizing a vol- ume distortion criterion. The result is a point-to-point map distorting linear cells into curved ones. Our method is based on several extensions of Voronoi Squared Distance Minimization (VSDM) combined with a higher-order finite element formulation of the deformation energy. This allows us to globally optimize the mapping without prior parameterization. The anisotropic VSDM formulation allows for sharp and semi-sharp features to be im- plicitly preserved without tagging. We use a hierarchical finite element function basis that selectively adapts to the geometric details. This makes both the method more efficient and the representation more compact. We ap- ply our method to geometric modeling applications in computer-aided design and computer graphics, including mixed-element meshing, mesh optimization, subdivision volume fitting, and shell meshing.
-
Fitting Polynomial Surfaces to Triangular Meshes with Voronoi Squared Distance Minimization
2011Co-Authors: Vincent Nivoliers, Dong-ming Yan, Bruno LévyAbstract:This paper introduces Voronoi Squared Distance Minimization (VSDM), an algorithm that fits a surface to an input mesh. VSDM minimizes an objective function that corresponds to a Voronoi-based approximation of the overall Squared Distance function between the surface and the input mesh (SDM). This objective function is a generalization of Centroidal Voronoi Tesselation (CVT), and can be minimized by a quasi-Newton solver. VSDM naturally adapts the orientation of the mesh to best approximate the input, without estimating any differential quantities. Therefore it can be applied to triangle soups or surfaces with degenerate triangles, topological noise and sharp features. Applications of fitting quad meshes and polynomial surfaces to input triangular meshes are demonstrated.
-
IMR - Fitting Polynomial Surfaces to Triangular Meshes with Voronoi Squared Distance Minimization
Proceedings of the 20th International Meshing Roundtable, 2011Co-Authors: Vincent Nivoliers, Dong-ming Yan, Bruno LévyAbstract:This paper introduces Voronoi Squared Distance Minimization (VSDM), an algorithm that fits a surface to an input mesh. VSDM minimizes an objective function that corresponds to a Voronoi-based approximation of the overall Squared Distance function between the surface and the input mesh (SDM). This objective function is a generalization of Centroidal Voronoi Tesselation (CVT), and can be minimized by a quasi-Newton solver. VSDM naturally adapts the orientation of the mesh to best approximate the input, without estimating any differential quantities. Therefore it can be applied to triangle soups or surfaces with degenerate triangles, topological noise and sharp features. Applications of fitting quad meshes and polynomial surfaces to input triangular meshes are demonstrated.
Gilad Yahav - One of the best experts on this subject based on the ideXlab platform.
-
the Squared Distance approach to frequency domain time resolved fluorescence analysis
Journal of Biophotonics, 2019Co-Authors: Gilad Yahav, Hilel Hagai Diamandi, Eyal Preter, Dror FixlerAbstract:A frequency-domain (FD) analysis of fluorescence lifetime (FLT) is a unique and rapid method for cellular and intracellular classifications that can serve for medical diagnostics purposes. Nevertheless, its data analysis process demands nonlinear fitting algorithms that may distort the resolution of the FLT data and hence diminish the classification ability of the method. This research suggests a sample classification technique that is unaffected by the analysis process as it is based on the Squared Distance (D2 ) between the raw frequency response data (FRD). In addition, it presents the theory behind this technique and its validation in two simulated data sets of six groups with similar widely and closely spaced FLT data as well as in experimental data of 43 samples from bacterial and viral infected and non-infected patients. In the two simulated tests, the classification accuracy was above 95% for all six groups. In the experimental data, the classification of 41 out of 43 samples matched earlier report and 29 out of 31 agreed with preliminary physician diagnosis. The D2 approach has the potential to promote FD-time resolved fluorescence measurements as a medical diagnostic technique with high specifity and high sensitivity for many of today's conventional diagnostic procedures.
-
The Squared Distance approach to frequency domain time‐resolved fluorescence analysis
Journal of biophotonics, 2019Co-Authors: Gilad Yahav, Hilel Hagai Diamandi, Eyal Preter, Dror FixlerAbstract:A frequency-domain (FD) analysis of fluorescence lifetime (FLT) is a unique and rapid method for cellular and intracellular classifications that can serve for medical diagnostics purposes. Nevertheless, its data analysis process demands nonlinear fitting algorithms that may distort the resolution of the FLT data and hence diminish the classification ability of the method. This research suggests a sample classification technique that is unaffected by the analysis process as it is based on the Squared Distance (D2 ) between the raw frequency response data (FRD). In addition, it presents the theory behind this technique and its validation in two simulated data sets of six groups with similar widely and closely spaced FLT data as well as in experimental data of 43 samples from bacterial and viral infected and non-infected patients. In the two simulated tests, the classification accuracy was above 95% for all six groups. In the experimental data, the classification of 41 out of 43 samples matched earlier report and 29 out of 31 agreed with preliminary physician diagnosis. The D2 approach has the potential to promote FD-time resolved fluorescence measurements as a medical diagnostic technique with high specifity and high sensitivity for many of today's conventional diagnostic procedures.
-
Frequency domain analysis of time-resolved fluorescence measurements using the Squared Distance approach
Nanoscale Imaging Sensing and Actuation for Biomedical Applications XVI, 2019Co-Authors: Gilad Yahav, Hilel Hagai Diamandi, Eyal Preter, Dror FixlerAbstract:Frequency domain analysis of time-resolved fluorescence measurements (TRFM) is an extremely rapid technique for medical diagnostics thanks to its unique sensitivity to a wide variety of physical and chemical features. Nevertheless, the determination of the underlying fluorescence lifetime (FLT) data of samples by their frequency response data (FRD), demands fitting algorithms. Therefore, the interpretation of the precise changes in the FLT of complex environments in term of biochemical processes is a challenge as it involves uncertainties associated with the chosen fitting algorithm. This research suggests a novel characterization procedure based on the Squared Distance (D2) between the FRD of the samples that avoid the inherent blurring caused by the transformation of the FRD into FLT data. The D2 approach was validated through simulated data of 6 classes with similar FLT characteristics, where the accuracy of D2 classification was about 96%. In addition, this approach was tested on experimental FRD from 43 individual samples that their preliminary physician diagnosis divided them into 4 groups: 5 healthy samples served as controls, 9 samples diagnosed with diverse types of bacteria, 16 samples diagnosed with diverse types of viruses and 13 samples were negatives to any bacterial or viral infection, although presenting related symptoms. Using the D2 analysis, the classification of 28/30 matched the physician diagnosis and the classification of 41/43 samples matched earlier report. In conclusion, this work demonstrated that the D2 model can aid in disease identification and increase the specificity and sensitivity of conventional medical procedures or TRFM-based diagnosis.